Modelling of a pressure swing adsorption unit by deep learning and artificial Intelligence tools

• Type: Article in International Scientific Journal
• Date: 2020

Authors

Oliveira, LMC (Author) Other; Koivisto, H (Author) Other; Iwakiri, IGI (Author) Other; Loureiro, JM (Author) FEUP; Ana M. Ribeiro (Author) FEUP; Nogueira, IBR (Author) FEUP

Journal

Title: Chemical Engineering Science

Vol. 224

ISSN: 0009-2509

Publisher: Elsevier

Indexing

ISI Web of Knowledge - 0 Citations

Scopus - 6 Citations

Associated Projects

LSRE-LCM - Laboratório de Processos de Separação e Reacção - Laboratório de Catálise e Materiais

Other information

Authenticus ID: P-00S-5SA

DOI: 10.1016/j.ces.2020.115801

Abstract (EN): Syngas is one of the main sources available for the production of pure H2 and synthetic fuels, among others. Pressure swing adsorption (PSA) is considered to be an efficient alternative for pre-treatment of syngas. However, it displays very complex dynamical behaviour. This work proposes the development of different Artificial Intelligence based models for the prediction of the dynamic behaviour of several process output variables. A classical model of ANNs, a machine learning model and a deep learning model was here developed. It was found that Deep Learning networks were the only ones capable of fully representing the dynamic behaviour of the PSA unit, whereas the other models were only partially capable of predicting it. Thus, it is proposed a reliable real-time soft sensor for a PSA unit based on Deep Leaning strategy. This strategy provides bases to overtake several problems associated to this processes control, operation and optimization. © 2020 Elsevier Ltd

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 17